Dynamic Simulation of a Multichamber CVD Cluster Tool techrep.Ulvac dyn... · Dynamic Simulation of a Multichamber CVD Cluster Tool N. Gupta(a), Y. Xu, L. Henn-Lecordier, T. Gougousi,

ulvacsim.paper.doc 08/20/99 p. 1 of 23

Dynamic Simulation of a Multichamber CVD Cluster Tool

N. Gupta(a), Y. Xu, L. Henn-Lecordier, T. Gougousi, J. N. Kidder, Jr., and G. W. Rubloff

Institute for Systems Research and Department of Materials and Nuclear EngineeringUniversity of Maryland, College Park, MD 20742-3285

Abstract

Dynamic, physically based simulation has proved very effective in representing the time-

dependent behavior of equipment, process, sensor and control systems. We have developed a

system-level dynamic simulator for an 8” CVD cluster tool (ULVAC-ERA1000). The simulator

incorporates models for equipment, process and control systems, representing time-dependent

system level behavior through the imposition of equipment controller recipes for pump-down,

process and venting cycles. Multistage pumping systems (Roots-mechanical and turbo-

mechanical) are modeled using compression ratios, nominal speeds, and volumes to reflect

actual behavior, validated against experiments on the Ulvac tool. The process simulator is

derived from the work of Hsieh et al. The Ulvac simulator is applied to optimization of

deposition rate and WF6 utilization, analysis of experimental data for recipe improvement, and

development and debugging of equipment controller software and operation.

a) Current address: Intel Corporation, Hillsboro, OR


IntroductionThe National Technology Roadmap for Semiconductors1 asserts strongly that continuing

progress in integrated circuit technology is increasingly dependent on enhanced equipment

productivity, involving costs of capital equipment, maintenance, and consumables, along with

improved equipment reliability and short time-to-repair. The importance of these factors is

underscored by the observations that equipment costs dominate the cost of new multi-billion

dollar manufacturing plants, and that focus on these issues has been formalized and

emphasized through the use of the concepts of cost-of-ownership (COO) and overall equipment

effectiveness (OEE).

Stimulated by the successes of technology modeling at the process, device, and circuit

levels, significant attention has been devoted to equipment or reactor modeling through

approaches such as computational fluid dynamics 2,3,4. These have been coupled through

explicit process models to reactor performance and the prediction of the evolution of

microfeature profiles5,6,7,8,9,10. However, the dynamics of equipment behavior through a process

cycle has been treated in only isolated cases, most notably to meet the challenge of rapid

thermal processing 11,12.

In this paper, we have studied the behavior of a multi-chamber cluster tool at the system

level. System-level dynamic simulation provides a powerful technique for studying the time-

dependant behavior of process, equipment, sensors and control systems of a manufacturing

tool. This has been shown by Lu et al.13,14 through simulation of polysilicon RTCVD processes.

Their results demonstrate that system level simulation allows the evaluation of not only the

deposition-related parameters, but also a broad range of system properties like equipment

performance, gas and heat flow conditions and process cycle time. We have developed a

similar model for a tungsten CVD cluster tool, which uses reduced order physical and chemical

models to describe the gas transport, heat transfer and deposition kinetics in the reactor. The

simulator captures the behavior of a full-scale manufacturing tool with multiple chambers (two

reactors, a load lock and a central wafer handler), and a multi-pump vacuum system. The gas

flow model for such a system must integrate the functioning of turbo and mechanical pumps in

a methodology consistent with behavior of a sequential pump system. The individual elements

representing equipment, process and control systems are integrated by the implementation of


process recipes which model the behavior of the cluster tool controller. The simulator has been

validated with experimental results, and is being used to investigate the dynamics and control of

deposition in tungsten CVD processes.

BackgroundSystem level dynamic simulation has been previously used by Lu et al.8 to study the

dynamics and control of polysilicon RTCVD. The RTCVD equipment consists of the gas

handling system, a single wafer reactor and reactor pumping station and a two-stage

differentially pumped QMS system. The simulator is composed of five elements. Since the

present work builds upon this structure, it is useful to understand the purpose of these

components in the polysilicon RTCVD simulator. The Process Recipe defines the status of the

valves, mass flow controllers (MFC’s), power to the heating lamps and overall process

conditions as a function of time. The Equipment Simulator uses the status of the valves, MFC’s

etc to calculate the gas flow and heat flow conditions in the reactor. The mathematical models

of the gas transport and heat transfer are based on reduced order mass and energy balance

equations. The outputs of the Equipment Simulator are the reactor pressure and the wafer

temperature. The Sensors and Control Systems simulator reads in this pressure and

temperature and compares them to the process parameters preset in the deposition recipe. It

then regulates the reactor pressure by changing the position of the throttle valve and the wafer

temperature by varying the power to the heating lamp, and thus the nominal process conditions

are established. The deposition kinetics are modeled in the Process Simulator which calculates

growth rates and film thickness, which are the final output of the integrated model.

Ulvac ERA-1000 Equipment DescriptionThe ULVAC-ERA 1000 at the Laboratory for Advanced Materials Processing, University of

Maryland at College Park, is a CVD cluster tool designed for tungsten deposition. It consists of

a gas handling system, the CVD reactor (including a load-lock, central wafer handler, two

reaction chambers and heating lamps), reactor pumping system and equipment controls. Fig. 1

shows the schematic layout of the cluster tool.

The gas handling system consists of mass flow controllers, which regulate the flow of the

reactant gases (WF6 and H2 for our deposition process) to the reaction chambers. The reaction

chambers are cylindrical in shape and are located symmetrically on either side of the central

wafer handler (CWH), which is then linked to a load-lock (LL). The load lock isolates the CWH


from the room ambient via a gate valve (V3). The CWH contains a robot arm, which takes the

wafer from the LL and deposits it on a wafer lifter pin inside the reaction chamber via a gate

valve (V1 or V2). The pressure in the reactor chamber is measured using a capacitance

manometer. The pressure during deposition is controlled using a feedback loop to continuously

adjust the throttle valve position on the reactor pumping station while maintaining a constant

gas flow to the reactor. During deposition, the wafer chuck rotates at a speed of 5 rpm to

ensure uniform heating.

The ULVAC cluster tool has a multistage pumping system. Each reactor is connected to

two sets of pumps, which perform different function during the process cycle. One is used for

chamber evacuation and consists of a turbo pump (TMP1 & 2) backed by a rotary vane pump

(RP4). The second set of pumps, each consisting of a Roots blower (RBP1 & 2) backed by a

rotary pump (RP1 & 2), is used to maintain constant pressure during the deposition process.

Reactor pumping is switched from one pump stack to the other by the use of gate valves. The

LL and the CWH are jointly connected to a Roots blower (RBP3) backed by a rotary vane pump

(RP3).

Overall Simulator StructureThe CVD cluster tool is modeled in three sections: the gas handling system, the reactor

(including LL, CWH and two process chambers), and the pumping stations. Each section is

modeled in terms of three processes: gas flow, heat transfer and the reaction chemistry. Gas

flow calculations for the ULVAC cluster tool are based on mass balance in each piece of the

equipment. We have assumed ideal mixing between gases in the reactor to simplify our

computations. The total number of molecules in each chamber must equal the molecules input

to the chamber minus the molecules pumped out. For the reaction chambers the mathematical

expression is

where Pinit is the initial (base) pressure of the reaction chamber in Torr, Pfin is the final pressure,

Qin is the total throughput of gas (in Torr-liter/sec) entering the reactor, Qout is the throughput of

( ) )1(1

__ dtQQQQV

PP depletionreactantproductsreactionoutininitfin ∫ −+−+=


gas leaving the reactor, Qreaction_products is the throughput introduced due to product generation

and Qreatant_depletion denotes the gas throughput removed from the chamber due to reactant

depletion. The total and partial pressures of the reactor gases can thus be calculated at every

iteration of the simulation.

Equipment gas flow modeling

Previous models developed for system-level simulation of CVD reactors have assumed a

uniform pumping speed for the reactor pumping station, which is a good approximation of the

vacuum system behavior when the reactor is evacuated by a single pump. However, the

complexity of the pumping system on the ULVAC cluster tool necessitates a more detailed

modeling of the mechanical and turbo pumps. Inclusion of factors like the pump start-up time or

the gradual decay of pumping efficiency with decreasing pressure allows a better

representation of the equipment state.

Heat transfer modeling

Heat flow calculations in our simulation are based on reduced-order energy balance

equations. The wafer is assumed to absorb power from the heating lamps primarily by radiation,

with corrections for conductive and convective losses. Wafer temperature is regulated by

controlling the power to the heating lamps. The energy balance for the wafer can be expressed

as:

Where aQA corresponds to the power absorbed by the wafer, 2eAσT4w is the power radiated by

the wafer, Wcc is the power lost by conduction and convection and mCp (dTw/dt) is the power

consumed for heating the wafer; a is the wafer absorptivity; Q is the incoming lamp radiation; e

is the wafer emissivity; A is the wafer area, σ is the Stefan-Boltzmann constant; Tw is the wafer

temperature; m is the mass of the wafer; Cp is the specific heat of the wafer material and dTw/dt

is the rate of heating of the wafer. Wcc is modeled as a linear function of the difference between

the wafer temperature and the ambient temperature.

)2(2 4

dt

dTmCWTeAaQA w

pccw =−− σ


Chemical kinetics modeling

The chemical kinetics for the film deposition are calculated by the Process Simulator,

using as inputs the partial pressures of the process gases and the wafer temperature. Due to

the cold wall configuration of the reactor, and the relatively low temperatures of operation (400-

550 C), we have excluded gas phase reactions in the kinetic model. The surface reactions for

tungsten deposition from WF6 and H2 gas have been adapted from Hsieh’s model 9,10 for the

reduction of WF6 by SiH4 followed by H2.

Process recipe modeling

Equipment operation and process dynamics are integrated by the implementation of a

process recipe, which models the process sequence and the timing of the equipment controller.

This helps to understand the complex dynamics of a manufacturing tool and to explain

irregularities in equipment behavior by allowing one to study a broad range of system

properties.

Simulator developmentGas flow in a multistage pumping system

Mechanical pumps like the rotary vane pump (RP1, 2, 3 & 4) operate in the low to medium

vacuum region and reach full speed almost instantly on being turned on at atmospheric

pressure. Fore pumps like Roots blowers (RBP1, 2 & 3) or turbo pumps (TMP1 & 2) have a

longer start-up time because they do not reach their optimal speed unless the backing pressure

is less than 10 mtorr. Thus they are typically backed by a mechanical pump (rotary vane pump

in this case) to pump down the fore line to the mtorr range 15. We have taken into account this

time delay by empirically modeling the ramp-up of pump speed with decreasing pressure in the

foreline. The variation of pumping speed with pressure at the high end of the fore pump

operation range is shown in Fig. 2

At the low end of the pump operation range, all mechanical and turbo pumps (which use

variations of rotating fans or turbine blades to pump gas molecules from one end of the pump

to another) reach a limit in their pumping capacity before they reach their lowest pressure. The

lowest pressure ideally achievable by such pumps is a called the base pressure (usually

specified by the pump manufacturer) and is the parameter taken into consideration when

designing vacuum systems. However, when the inlet of the pump reaches a low pressure, gas


particles start backstreaming from the pump exhaust towards the inlet. Once the flux of the

backstreaming molecules equals the flux in the forward direction, a steady state pressure,

which is the actual base pressure is established.

This backstreaming of gas particles is measured in terms of a compression ratio, which is

defined as the ratio of the outlet pressure to the inlet pressure of a pump 11. The compression

ratio depends on the speed and design of the turbine blades, and varies with the size of the

species being pumped, i.e. light gases like hydrogen and helium have much smaller

compression ratios than heavy gases like argon, and are harder to pump on. If the compression

ratio is constant for a pump for a given process, lower base pressures can be achieved by

lowering the exhaust pressure, usually done by using a backing pump. In processes using a

combination of gases, the ultimate pressure is determined by the compression ratio of the

lighter gases.

The pressure-dependent pumping speed for each pump in the lower pressure regime has

been modeled in terms of the compression ratio of the pump. The effective gas throughput for a

pump is a combination of flow in the “forward” or inlet-to-exhaust direction and the flow due to

backstreaming particles.

Throughput in the forward direction: Qf (Torr-liter/sec) = (Ppump inlet – Pbase) * PS (3)

Throughput due to backstream: Qb (Torr-liter/sec) = (Poutlet– Ppump inlet) * PS / CR (4)

(in the reverse direction)

Effective throughput: Qeff = [(Ppump inlet – Pbase) - (Poutlet– Ppump inlet) * 1/ CR]*PS (5)

where Ppump inlet is the pressure at the chamber being evacuated. For the fore pumps (RBP and

TMP), Ppump inlet is the pressure at the reaction chamber, and for the rotary vane backing

mechanical pump (RP), it is the pressure at the outlet of it’s fore pump. Poutlet is the pressure at

the exhaust of the pump. The fore pumps exhaust into the inlet of the backing pumps, which in

turn pump out to atmosphere. Pbase is the theoretical base pressure of the pump, which would

have been in the absence of backstreaming gas. PS is the maximum speed of the pump, which

it achieves at its optimal pressure range. CR is the compression ratio, which is usually specified

by the manufacturer for a particular gas.


Thus the effective pumping speed in the low pressure end of the pump’s operating range

is not PS but is given by

Modeling the transient behavior of process pumps has a significant impact on the total process

cycle time in a multi-pump setup. Because of the sequential operation of the pumps, the start-

up time for the fore pumps depends on the lowest pressure achieved by the mechanical

backing pumps. Secondly, the conductance of the process pumps is limited by the fact that

during tungsten deposition, the bulk of the reactor gas is composed of hydrogen, which has a

very small compression ratio. This could affect the time response of the throttle valve to

pressure changes during the process.

Chemical Kinetics Model for W CVD

We consider here the Hsieh model 9,10 for the surface reduction of WF6 by (SiH4 + H2),

which has three competing reaction pathways of the Eley-Rideal type. The first pathway

describe the SiH4 reduction of WF6, the second pathway the Si reduction of WF6, and the third

the H2 reduction of WF6

We have modified the Eley-Rideal mechanism to obtain a three step kinetic model for the

surface reduction of WF6 by H2. In the first step, WF6 gas is reduced by surface silicon to form an

initial seed layer of tungsten. In the next step, WF6 gas adsorbs on to active tungsten sites.

Finally, adsorbed WF6 is reduced by H2 to deposit W metal. The relevant equations are given

below:

Nucleation: WF6 + 3/2 Si* È W* + 3/2 SiF4 (g) R1 = 0.3 JWF6 θSi (7)

Adsorption: W* + WF6 È WF6* R2 = 0.48 JWF6 θW (8)

Reduction: WF6* + 3H2 È W(s) + 6HF R3 = kH2 [PH2]0.5 θWF6 (9)

Site conservation: θSi + θWF6 + θW*=1 (10)

where kH2 (mol/cm2/s.Torr0.5) = 0.0083 exp[-8800/T(K)] is an equipment dependent empirical

constant depending on the process temperature. JWF6 is the molar flux of WF6 gas, [PH2] is the

)6(]1

)(

)(1[)/( PS

CRPP

PPslitersPS

baseinletpump

inletpumpoutleteff ××

−−

−=


partial pressure of H2 in the reactor and θSi, θW and θWF6 are the fractions of the total surface

active sites covered by Si*, W* and WF6* respectively. The rates of the first two reactions are

proportional to the sticking probabilities (0.3 and 0.48 respectively) of the impinging gas on the

active site.

The dynamic rates of the reactions are calculated in terms of the instantaneous

concentrations of the reactant gases and surface sites. In turn, the surface coverage of Si, W

and WF6 can be calculated by integrating over time the rates of all the reaction steps that

produce or consume the particular species, at each iteration step. The net deposition rate Rnet

equals (R1+R3), since both reactions deposit tungsten metal on the surface. The total film

thickness is obtained by integrating the growth rate over time.

Initially, the surface coverage (θSi) of the Si sites is 1. Due to low activation energy, the

nucleation reaction takes place at a very high rate and rapidly drives θSi to zero. For simplicity,

diffusion of Si atoms through the W film is not included in the simulator. Once the initial seed

layer of W* is deposited covering all available Si sites, R1 becomes zero. Shortly after, R2 and

R3 reach a steady-state equilibrium, i.e. R2=R3, and the bulk of the W film is deposited.

The deposition of W films using H2 reduction of WF6 has traditionally been regarded as a

slow and inefficient process. According to the simulator, in the pressure range of 0.1-0.5 Torr

and temperatures around 400-500 C, less than 10% of the WF6 is deposited on the wafer. It is

known that the deposition rate as well as the WF6 conversion rate can be significantly

enhanced with the introduction of small amounts of silane (SiH4).

Process Recipe for Model Integration

The various elements for gas and heat flow, deposition kinetics etc. have been integrated

into a coherent unit in order to achieve a system-level representation of the cluster tool. This

has been done in two steps. First, the controller command sequence and interlocks were

duplicated in as much detail as possible, to allow us to simulate the functioning of a full-scale

manufacturing tool. Thus the Process Recipe module defines valve states and interlocks and

MFC states, which represent equipment states, such as venting the load lock and wafer handler

to load the wafer, pumping down all chambers prior to deposition, opening up mass flow

controllers and turning on the heating lamp to begin deposition. The status of valves, MFC’s

and heating lamps is used to compute gas and heat transport in the system.


Secondly, to simulate a deposition process, a number of process parameters must be

entered by the user. Numerical values for the flow rates of the process gases, process

pressure, temperature and timing for each step of the deposition recipe (if it is a multi-step

recipe) form the complete set of inputs. Once the simulation is initiated, the wafer is loaded into

the reactor via the load lock and the central wafer handler. When the reactor is ready for

deposition, the gas flow, pressure and temperature control systems implement a series of

actions to achieve and maintain the set values of deposition parameters. A detailed modeling of

the equipment controller aids significantly in understanding and analyzing the dynamics of the

cluster tool at every step of its operation.

ValidationThe various modules of the ULVAC cluster tool simulator have been validated

independently to create a tool which can be directly used for experimental design and process

control strategies.

To validate the simulation results for equipment dynamics we developed a data

acquisition system (using DAQ cards from Computer Boards) to read relevant analog and

digital voltage signals (for process pressure, valve status and conductance, wafer temperature

etc) from the equipment controller. Once the voltage signals were scaled to range of the

respective parameter, they were directly compared to the simulation results. Fig. 3 presents

simulation results for the reactor pressure and the status (on/off) for valves V11 and V41 (see

Fig. 1) as a function of time. Data is shown for the wafer loading part of the process cycle. The

valve connecting the reactor to the process pumps (V11) is closed when the wafer is being

loaded in through the load lock and central wafer handler. Once the wafer is loaded the reactor

is pumped down using the turbo pumps (V41) to the 10-6 Torr range to remove all

contaminants. After the successful completion of this step, valve V11 opens up as deposition

commences. This detailed modeling of equipment behavior has been instrumental in analysis

and optimization of system behavior.

In order to validate the accuracy of implementation of Hsieh’s model in the simulator, the

simulated results for film growth rate have been compared to the experimental results reported

by Rosler and co-workers16. Fig. 4 shows our simulation results for the tungsten deposition

process, which seem to be in good agreement with experimentally measured growth rates.


Applications of Dynamic Cluster Tool SimulatorOptimization of Deposition Processes

An experimentally validated dynamic simulator proves to be an invaluable tool for process

recipe improvement and optimization. A primary application of the ULVAC cluster tool has been

in defining a process regime for tungsten deposition, which would optimize both the growth rate

and reactant consumption. To target an optimal process pressure and reactant composition

which would generate improved process recipes, we simulated deposition processes for

different H2:WF6 flow rate ratios and process pressures (0.2 and 0.5 torr), for the temperature

range 350-600 °C. In each case the flow rate of H2 is kept fixed at 200 sccm, and the results

are shown in Fig. 5.

Simulation results showed that although the conversion rates for WF6 are better for high

ratios of H2:WF6, deposition rates can be enhanced by moving towards a hydrogen limited

regime (low H2:WF6 ratio), which is also in favor of uniform W thin film. Increasing the process

pressure benefits both the film growth rate and the conversion ratio. This behavior can be

understood as arising from a longer residence time for the reactant gases in the reactor. For

the same reactants inlet rates, if the process pressure is increased, the throttle valve

conductance must decrease to reach the set-point pressure. This means that the gas

molecules remain in the reactor for a longer time, increasing the probability of their impinging

on the wafer surface and thus increase the growth rate and the conversion ratio as well. Once

simulation results have been used to define the desired process regime, smaller and better-

focussed sets of actual experiments may be designed for optimal process conditions.

Equipment Diagnostics and Fault Detection

A dynamic simulator can generate the time-dependent response of all the process and

equipment related parameters through the process cycle. This can be compared against the

signals from the equipment sensors to detect and analyze dynamic details of system operation.

We have used this capability explicitly to debug and better understand the subtleties of the

cluster tool, particularly the relative timing of actuation events involving valves, flow controllers,

etc. The integration of multiple equipment state signals, and their time-synchronous

combination with chemical sensor signals, has revealed important but subtle feature in the

equipment dynamics, while the dynamic tool simulator has been the primary vehicle which

enables interpretation and understanding of the system dynamics.


An example of this simulator application is illustrated in Fig. 6. The experimental data

showed that at the end of the deposition process (when the wafer was being transferred from

the reactor back to the central wafer handler through the slit valve) the pressure in the reactor

shot up briefly in a sharp spike. This seemed abnormal since there should have been no

pressure gradient between the reactor and the CWH during wafer transfer. In an attempt to

explain this phenomenon, we modified the Process Recipe module to simulate the following

sequence: during the removal of the wafer from the reactor, the mass flow controllers would

simultaneously open up completely for a second to flush out all the process gases. The

resulting brief pressure spike in the reactor corresponded closely to experimental data obtained

from the cluster tool, as seen in Fig. 6. Thus it is the combination of sensor integration and

dynamic simulation, which provides a powerful tool for system analysis and potential

improvement.

Test-bed for Equipment Controller Development

Finally, we have begun to exploit the dynamic simulator as a vehicle for equipment

controller development. In order to enhance the versatility of equipment control for

implementing various approaches to advanced process control, we have been bringing up a

new tool control system based on a Brooks Automation platform. Just as in the design of any

new equipment control system, a major challenge is to develop and validate the software

recipes in the control system. If the real equipment must be the test-bed for control software

debugging, significant cost and delay in equipment usage will result. The object here is to

exploit the dynamic simulator for validating and debugging control software, rather than

requiring dedication of real hardware to the task.

The ULVAC simulator has been designed so that the Process Recipe module can be

isolated and/or disconnected from the equipment and process modules, thereby allowing the

simulator to be operated in a fully manual way, i.e., one actuator (valve, flow controller, etc.) at

a time. This makes the simulator a "virtual tool" operable by an external agent (i.e., a user or

some other control system). We added a real-time interface (hardware interface cards and

their software support) to the VisSim simulator, so that simulator PC could sense and respond

to electrical actuation signals from the Brooks controller, and also return electrical signals from

the simulator to the controller. We were then able to use the Brooks controller, and its software

for process recipes, to control the ULVAC dynamic simulator, and thereby to see how the

system - represented faithfully by the dynamic simulator - behaved under the control system

process recipes in the Brooks controller.


Summary and ConclusionsWe have developed a system level simulator for the deposition of tungsten in a multi-

chamber CVD cluster tool. The overall architecture provides for the incorporation of

manufacturing equipment dynamics as well as reduced order process models in a coherent

manner. The individual modules have been integrated through the imposition of equipment

controller recipes for the cluster tool operation. The Process Recipe determines the state of the

valves, flow controllers, and heating lamps at each step of the process cycle, and defines a

sequence of actions for the equipment to achieve them. In this way, the dynamics of a

manufacturing process tool have been simulated at the system level. Validation of the dynamic

simulator has been accomplished by comparison with experimental data for individual simulator

components and more complex system-level behavior.

This dynamic simulator extends previous work 13,14 by providing a more sophisticated

representation of pumping systems, specifically multistage pumping arrangements, and by

representing a full multichamber cluster tool. In this work, we have seen its applications to initial

process optimization, interpreting important subtleties of equipment dynamics, and testing of

control system software and operation. Given the manufacturing importance attributed to the

details of equipment behavior - and particularly dynamics - this research direction appears of

substantial promise to impact a variety of equipment-centered manufacturing issues.

AcknowledgementsWe appreciate valuable discussions with B. Conaghan, S. Butler, E. Hall, and M.

Hanssmann. This work has been supported in part by Texas Instruments, the Semiconductor

Research Corporation, and the National Science Foundation.


References 1 National Technology Roadmap for Semiconductors 1997 (SIA Semiconductor Industry

Association, San Jose, CA, www.semichips.org, 1998).2 E. Sachs, G. H. Prueger, and R. Guerrieri, "An equipment model for LPCVD", IEEE Trans.

Semiconduct. Manuf. 5, p. 3 (Feb, 1992).3 C. R. Kleijn, "The modeling of LPCVD in single-wafer reactors as a tool for process

optimization and equipment design", J. de Physique IV, 1 (C2), pp. 19-31 (Sept. 1991).4 C. R. Kleijn, C. J. Hoogendoorn, A. Hasper, J. Holleman, and J. Middelhoek, "Transport

Phenomena in Tungsten LPCVD in a Single-Wafer Reactor", J. Electrochem. Soc. 138 (2)(Feb., 1991).

5 T. S. Cale, V. Mahadev, Z. Tang, G. Rajagopalan, L. J. Borucki, “Topography evolution duringsemiconductor processing”, Proceedings of NATO Advanced Study Institute, vol. 109-24,p. 613, (1997)

6 J. P. McVittie, J. C. Rey, A. J. Bariya, M. M. IslamRaja, L. Y. Cheng, S. Ravi, K. C. Saraswat,Proceedings SPIE Symposium on Advanced Techniques for Integrated Circuit Pocessing,vol. 1392, p. 126, (1990)

7 A. Hasper, J. Holleman, J. Middelhoek, C. R. Kleijn, and C. J. Hoogendoorn, "Modeling andOptimization of the Step Coverage of Tungsten LPCVD in Trenches and Contact Holes",J. Electrochem. Soc. 138 (6), 1728-38 (June 1991).

8 T. S. Cale, M. K. Jain, and G. B. Raupp, "Programmed Rate Processing to IncreaseThroughput in LPCVD", J. Electrochem. Soc. 137 (5), pp. 1526-33 (May 1990).

9 J. J. Hsieh, “Kinetic model for the chemical vapor deposition of tungsten in the silanereduction process”, J. Vac. Sci. Technol A 11 (6), pp. 3040-46, (Nov/Dec 1993).

10 J. J. Hsieh, "Influence of surface-activated reaction kinetics on low-pressure chemical vapordeposition conformality over micro features", J. Vac. Sci. Technol. A 11 (1), pp. 78-86(Jan/Feb 1993).

11 K. F. Jensen, and D. B. Graves, “Modeling and analysis of low pressure CVD reactors”, J.Electrochem. Soc. 130, p. 1950, (Sept. 1983)

12 F. Y. Sorrell, J. A. Harris, R. S. Gyurcsik, IEEE Trans. Semiconduct. Manuf., vol. 3, p. 183,(Nov. 1990)

13 G. Lu, M. Bora, L. L. Tedder, G. W. Rubloff, “Integrated dynamic simulation of rapid thermalchemical vapor deposition of polysilicon”, IEEE Trans. Semiconduct. Manuf. Vol. 11, p.63, (Feb. 1998)

14 G. Lu, M. Bora, G. W. Rubloff, “Polysilicon RTCVD process optimization for environmentally-conscious manufacturing”, vol. 10, p. 390, (Aug. 1997).

15 P. A. Redhead, J. P. Hobson, and E. V. Kornelson, “The Physical Basis of UltrahighVacuum”, Chapman and Hall, 1968.


16 R. S. Rosler, J. Mendonca, and M. J. Rice, “Tungsten chemical vapor depositioncharacteristics using SiH4 in a single wafer system”, J. Vac. Sci Technol. B 6 (6), p. 1721(1998).


Figure Captions

Figure 1. Schematic draws of the Ulvac ERA-1000 CVD cluster tool. Reactors 1 and 2 are

pumped by Roots pumps RBP1 and RBP2 backed by mechanical pumps RP1 and

RP2 during processing, while they are pumped to base pressures prior to process by

turbopumps TMP1 and TMP2, backed by mechanical pump RP4. Mechanical pump

RP3 is used to evacuate the load lock. The various chambers are isolated from each

other by slit (gate) valves V1, V2, and V3.

Figure 2. Variation of pumping speed with system pressure for turbo and mechanical pumps.

Figure 3. Comparison of reactor pressure and valve state signals during the initial part of the

process cycle, i.e., wafer loading into the reactor for deposition. Data points are from

dynamic simulation, while solid lines are experimental measurements using

equipment state signal integration under LabView. Voltage signals for valve states

(∼3.0V) are scaled up by 100X for clarity. Valve state data from the simulation is

scaled vertically to match amplitude of experimental signals.

Figure 4. Comparison of simulated deposition rate with experimental results reported by Rosler

et. al. 16.

Figure 5. Analysis of deposition rate and WF6 conversion vs. temperature for different reactant

ratios (H2:WF6 flow rate ratio = 20:1, 10:1, 5:1) for process pressures (a) 0.2 Torr

and (b) 0.5 Torr.

Figure 6. Dynamic simulation (triangles) and experimental data (smooth curve) for pressure

burst observed in reactor during post-deposition wafer unload.


Figure 1. Schematic draws of the Ulvac ERA-1000 CVD cluster tool. Reactors 1

and 2 are pumped by Roots pumps RBP1 and RBP2 backed by

mechanical pumps RP1 and RP2 during processing, while they are

pumped to base pressures prior to process by turbopumps TMP1 and

TMP2, backed by mechanical pump RP4. Mechanical pump RP3 is used

to evacuate the load lock. The various chambers are isolated from each

other by slit (gate) valves V1, V2, and V3.

TMP2

RP4

TMP1

CENTRALWAFERHANDLER

REACTOR 2 REACTOR 1

LOADLOCK

TOSCRUBBER

V1V2

V3

PROCESSPUMPS

RBP1 &

RP1

PROCESSPUMPS

RBP2 &

RP2

RP3

V11

V41


Figure 2. Variation of pumping speed with system pressure for turbo and

mechanical pumps.∗

∗ A representative plot of the pumping speed as illustrated in ALCATEL ATP Catalog

Pu

mp

ing

sp

eed

100

Mechanical PumpTurbo Pump

10210-210-410-610-810-12 10-10

System pressure ( Torr )


Figure 3. Comparison of reactor pressure and valve state signals during the initial

part of the process cycle, i.e., wafer loading into the reactor for

deposition. Data points are from dynamic simulation, while solid lines

are experimental measurements using equipment state signal

integration under LabView. Voltage signals for valve states (∼3.0V) are

scaled up by 100X for clarity. Valve state data from the simulation is

scaled vertically to match amplitude of experimental signals.

60 80 100 120 140

0

100

200

300

400

500V11 closesfor wafer load

deposition starts

valve opens forreactor pumpdownbefore deposition rise in reactor press

during wafer transferfrom CWH to reactor

reactor pressure valve V11 status valve V41 status

Pre

ssu

re (

mT

orr

)V

alve

sta

te in

(vo

ltag

e si

gn

al*1

00)

V

Process time (s)


Figure 4. Comparison of simulated deposition rate with experimental results

reported by Rosler et. al. 16.

1.2 1.4 1.6 1.8100

1000

10000 experimental data

simulation results

Dep

osi

tio

n r

ate

in A

/min

1000/T (K-1)


Figure 5 (a). Analysis of deposition rate and WF6 conversion vs. temperature for

different reactant ratios (H2:WF6 flow rate ratio = 20:1, 10:1, 5:1) for

0.2 Torr pressure.

350 400 450 500 550 6000

10

20

30

40

50

Process temperature (°C)

Pressure: 0.2 TorrH2 flow rate: 200 sccm

WF6 = 10 sccm (20:1 ratio)WF6 = 20 sccm (10:1 ratio)WF6 = 40 sccm (5:1 ratio)

Co

nve

rsio

n f

or

WF

6 (%

)

350 400 450 500 550 6000

1000

2000

3000

4000

5000

6000

7000

WF6 = 20 sccm (10:1 ratio)




Dep

osi

tio

n r

ate

(A/m

in) Pressure: 0.2 Torr

H2 flow rate: 200 sccm


Figure 5 (b). Analysis of deposition rate and WF6 conversion vs. temperature

for different reactant ratios (H2:WF6 flow rate ratio = 20:1, 10:1, 5:1)

for 0.5 Torr pressure.

350 400 450 500 550 6000

1000

2000

3000

4000

5000

6000

7000

WF6 = 20 sccm(10:1 ratio)




Dep

osi

tio

n r

ate

(A/m

in)

350 400 450 500 550 6000

10

20

30

40

50





Co

nve

rsio

n f

or

WF

6 (%

)




Figure 6.Dynamic simulation (triangles) and experimental data (smooth curve) for

pressure burst observed in reactor during post-deposition wafer unload.

625 650 675 700 725

0

200

400

600

800

1000

wafer unload

pressure burst due toflushing of MFC valves

deposition processpressure = 900 mtorr

Rea

cto

r p

ress

ure

(m

To

rr)

Process time (s)

Documents

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